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from __future__ import annotations | |
import numpy as np | |
import pandas as pd | |
import requests | |
from huggingface_hub.hf_api import SpaceInfo | |
class PaperList: | |
def __init__(self): | |
self.organization_name = "ICML2022" | |
self.table = pd.read_csv("papers.csv") | |
self._preprcess_table() | |
self.table_header = """ | |
<tr> | |
<td width="50%">Paper</td> | |
<td width="26%">Authors</td> | |
<td width="4%">pdf</td> | |
<td width="4%">arXiv</td> | |
<td width="4%">GitHub</td> | |
<td width="4%">HF Spaces</td> | |
<td width="4%">HF Models</td> | |
<td width="4%">HF Datasets</td> | |
</tr>""" | |
def load_space_info(author: str) -> list[SpaceInfo]: | |
path = "https://huggingface.co/api/spaces" | |
r = requests.get(path, params={"author": author}) | |
d = r.json() | |
return [SpaceInfo(**x) for x in d] | |
def add_spaces_to_table(self, organization_name: str, df: pd.DataFrame) -> pd.DataFrame: | |
spaces = self.load_space_info(organization_name) | |
name2space = {s.id.split("/")[1].lower(): f"https://huggingface.co/spaces/{s.id}" for s in spaces} | |
df["hf_space"] = df.loc[:, ["hf_space", "github"]].apply( | |
lambda x: ( | |
x[0] | |
if isinstance(x[0], str) | |
else name2space.get(x[1].split("/")[-1].lower() if isinstance(x[1], str) else "", np.nan) | |
), | |
axis=1, | |
) | |
return df | |
def _preprcess_table(self) -> None: | |
self.table = self.add_spaces_to_table(self.organization_name, self.table) | |
self.table["title_lowercase"] = self.table.title.str.lower() | |
rows = [] | |
for row in self.table.itertuples(): | |
paper = f'<a href="{row.url}" target="_blank">{row.title}</a>' | |
pdf = f'<a href="{row.pdf}" target="_blank">pdf</a>' | |
arxiv = f'<a href="{row.arxiv}" target="_blank">arXiv</a>' if isinstance(row.arxiv, str) else "" | |
github = f'<a href="{row.github}" target="_blank">GitHub</a>' if isinstance(row.github, str) else "" | |
hf_space = f'<a href="{row.hf_space}" target="_blank">Space</a>' if isinstance(row.hf_space, str) else "" | |
hf_model = f'<a href="{row.hf_model}" target="_blank">Model</a>' if isinstance(row.hf_model, str) else "" | |
hf_dataset = ( | |
f'<a href="{row.hf_dataset}" target="_blank">Dataset</a>' if isinstance(row.hf_dataset, str) else "" | |
) | |
row = f""" | |
<tr> | |
<td>{paper}</td> | |
<td>{row.authors}</td> | |
<td>{pdf}</td> | |
<td>{arxiv}</td> | |
<td>{github}</td> | |
<td>{hf_space}</td> | |
<td>{hf_model}</td> | |
<td>{hf_dataset}</td> | |
</tr>""" | |
rows.append(row) | |
self.table["html_table_content"] = rows | |
def render(self, search_query: str, case_sensitive: bool, filter_names: list[str]) -> tuple[int, str]: | |
df = self.add_spaces_to_table(self.organization_name, self.table) | |
if search_query: | |
if case_sensitive: | |
df = df[df.title.str.contains(search_query)] | |
else: | |
df = df[df.title_lowercase.str.contains(search_query.lower())] | |
has_arxiv = "arXiv" in filter_names | |
has_github = "GitHub" in filter_names | |
has_hf_space = "HF Space" in filter_names | |
has_hf_model = "HF Model" in filter_names | |
has_hf_dataset = "HF Dataset" in filter_names | |
df = self.filter_table(df, has_arxiv, has_github, has_hf_space, has_hf_model, has_hf_dataset) | |
return len(df), self.to_html(df, self.table_header) | |
def filter_table( | |
df: pd.DataFrame, | |
has_arxiv: bool, | |
has_github: bool, | |
has_hf_space: bool, | |
has_hf_model: bool, | |
has_hf_dataset: bool, | |
) -> pd.DataFrame: | |
if has_arxiv: | |
df = df[~df.arxiv.isna()] | |
if has_github: | |
df = df[~df.github.isna()] | |
if has_hf_space: | |
df = df[~df.hf_space.isna()] | |
if has_hf_model: | |
df = df[~df.hf_model.isna()] | |
if has_hf_dataset: | |
df = df[~df.hf_dataset.isna()] | |
return df | |
def to_html(df: pd.DataFrame, table_header: str) -> str: | |
table_data = "".join(df.html_table_content) | |
html = f""" | |
<table> | |
{table_header} | |
{table_data} | |
</table>""" | |
return html | |